The use of an on-board video camera and image processing of the roadway scenes allows useful information to be gathered for vehicle control. Detecting lane boundaries is a core capability for advanced automotive functions such as collision warning, collision avoidance and automatic vehicle guidance. If the lane boundaries and thus the road path can be detected several other functions can be implemented. Lane control uses the boundary information and vehicle dynamics knowledge to derive steering and braking commands for keeping the vehicle in the lane. Headway control uses a laser or radar system to track the vehicle ahead and keeps a safe driving distance. The lane boundary information can be used to prevent the detection of a vehicle in an adjacent lane on a curved road. Then the sensor beam can be directed to points within the lane. To monitor driving performance, the behavior of the driver is tracked and evaluated using the estimated position of the vehicle with respect to the lane boundaries.
Lane boundary detection for guiding vehicles along roadways has been reported in the paper by Dickmanns, E. D. and Zapp, A , "A Curvature-based Scheme for Improving Road Vehicle Guidance by Computer Vision," Proc. SPIE on Mobile Robots, Vol. 727, October 1986, which is incorporated herein by reference. Contour correlation and high order world models are the basic elements of that method, realized on a special multi-processor computer system. Perspective projection and dynamical models (Kalman filter) are used in an integrated approach for the design of the visual feedback control system. That system requires good lane markings and thus is limited to only those roads having good lane markings. It is of course desirable to extend the benefits of the computer vision system to roads with less good markings and to incorporate other features such as obstacle detection.